Linear SVM-based recognition of elementary juggling movements using correlation dimension of Euler Angles of a single arm

Recognizing the human arm movements has several applications, and it can be performed in a number of ways through the use of one or more sensor devices that the technology offers. This paper aims to exploit the exercises performed by jugglers in order to recognize the arm movements on the basis of the only information on the arm orientation provided by the Euler Angles. The proposed recognizer has two modules, i.e., a feature extractor and a classifier. The former reconstructs the dynamics of the system and estimates three correlation dimension s, each associated with a given Euler Angle. The latter is formed by a Linear Support Vector Machine. Extensive experimentations show the effectiveness of the proposed approach.